Lab-on-Sensor for Structural Behavior Monitoring: Theory and Applications

There are over 600,000 bridges in the U.S. National Bridge Inventory (NBI). Nearly 50% of them rapidly approach their design life and deteriorate at an alarming rate, particularly under an increasing volume of overweight trucks. Visual inspection as the current practice in bridge management is labor intensive and subjective, resulting in inconsistent and less reliable element ratings. Lab-on-sensor technologies can provide supplemental mission-critical data to the visual inspection for both qualitative and quantitative evaluations of structural conditions, and thus critical decision-making of cost-effective strategies in bridge preservation. In this presentation, the design and operation characteristics of highway bridges are first reviewed to establish the needs for structural behavior monitoring in order to align monitoring outcomes with daily practices in bridge preservation. Next, a lab-on-sensor design theory is presented and applied to detect and assess structural behaviors such as concrete cracking, foundation scour, and steel corrosion. Finally, the accuracy, resolution and measurement range of various sensors are discussed before this presentation is concluded

UAV-enabled Measurement for Spatial Magnetic Field of Smart Rocks in Bridge Scour Monitoring

Foundation scour is the main cause of bridge collapses in the U.S. In 2011, the principal investigator (PI) proposed smart rocks with embedded magnets for bridge scour monitoring. Once deployed around a bridge pier, smart rocks as field agents offer mission-critical information about the maximum depth of a scour hole developed around the bridge foundation – the key parameter that is used to assess foundation stability in engineering design and retrofit. Smart rocks have recently been deployed and tested at three bridge sites in California and Missouri. With multiple measurements, they can be located with an accuracy of 0.5 m. This level of performance, however, largely depends on the availability of a crane that extends the measurement station from the deck of a bridge to the proximity of a smart rock. The use of the crane often requires traffic closure and, more importantly, limits the number of measurement points and thus makes the detection of two or three smart rocks practically impossible. This project aims to develop a moving unmanned aerial vehicle (UAV) platform for the magnetic field measurement with and without smart rocks, and characterize the field performance of smart rocks so that the smart rock technology can be tested to its full potential for real time monitoring of bridge scour during significant flood events

UAV-enabled Measurement for Spatial Magnetic Field of Smart Rocks in Bridge Scour Monitoring

This lecture will present an overview of the research, development, validation, and implementation of \u27smart\u27 rocks as in-situ agents to assist in remote monitoring of bridge scour in real time. It will start with a brief review of fundamental concepts such as magnet, polarization, magnetic field, and field measurement principle. It will then introduce the concept of ‘smart’ rocks, demonstrate it through small-scale laboratory tests, and design and fabricate gravity-controlled ‘smart’ rocks for field implementation based on river hydrodynamics and riverbed conditions. Next, a ‘smart’ rock localization optimization algorithm will be formulated analytically and validated experimentally in open fields. It will be followed by the integration of magnetic field measurements into a mobile unmanned aerial vehicle (UAV) including a global positioning system. Finally, this lecture will present the field test and simulation results at a bridge site and data interpretation to determine a critical engineering parameter - maximum scour depth in the past three years. Overall, a properly-designed ‘smart’ rock consistently moved down the bottom of a scour hole through repeated laboratory tests. Each UAV-supported field test lasted for about 10 minutes. The ‘smart’ rock positioning at the bridge site is consistent with an accuracy of approximately 0.3 m. ‘Smart’ rocks is a promising technology to mitigating the effects of bridge scour, which is the main reason for the collapsing of over 1,500 bridges in the U.S

Probability of Detection in Structural Health Monitoring

The fundamental concept of the probability of detection in structural health monitoring is introduced. The traditional Probability of Detection (POD) method as described in the Department of Defense Handbook MIL-HDBK-1823A for nondestructive evaluation systems does not take the time dependency of data collection into account. When applied to in-situ sensors for the measurement of flaw sizes, such as fatigue-induced crack length and corrosion-induced mass loss, the validity and reliability of the traditional method is unknown. In this 50-minute lecture, the POD for in-situ sensors and their associated reliability assessment for detectable flaw sizes are evaluated using a Flaw-Size-at-Detection (FSaD) method and a Random Effects Generalization (REG) model. Although applicable to other sensors, this presentation is focused on long period fiber gratings (LPFG) corrosion sensors with thin Fe-C coatings. The FSaD method uses corrosion-induced mass losses when successfully detected from different sensors for the first time, while the REG model considers the randomness and difference between mass loss datasets from different sensors. The Fe-C coated LPFG sensors were tested in 3.5 wt.% NaCl solution until the resonant wavelength of transmission spectra no longer changed or the Fe-C coating was oxidized completely. The wavelength shift of 70% of the tested sensors ranged from 6 to 10 nm. In comparison with the FSaD method, the REG method is more robust to any departure from model assumptions since significantly more data are used in the REG method

Pooled-Fund Project No. TPF-5(395)

The goals of the pooled-fund initiative are to engage closely with several state Departments of Transportation (DOTs) in the early stage of technology development at the INSPIRE University Transportation Center, and leverage the center resources to develop case studies, protocols, and guidelines that can be adopted by state DOTs for bridge inspection without adversely impacting traffic. The initiative involves the integration, field demonstration and documentation of a robotic system of structural crawlers, unmanned aerial vehicles, a multimodal unmanned vehicle, nondestructive devices, sensors, and data analytics. Depending on the interest of participating DOTs, the objectives of this initiative include, but are not limited to: Development of inspection/operation protocols for various types of bridges with the robotic system integrated into current practice. Comparison and correlation of bridge deck inspections from the top and bottom sides of decks to understand the reliability of traffic disruption-free bridge inspection from the underside of decks. Design and technical guidelines of measurement devices on a robotic platform for the detection of surface and internal damage/deterioration in structural elements, and for the change in lateral support of foundations. Data fusion and analytics of measurements taken from various imaging and sensing systems for consistency and reliability. Development of best practices on bridge inspection using the robotic system

Hyperspectral Image Analysis for Mechanical and Chemical Properties of Concrete and Steel Surfaces

A typical human eye will respond to wavelengths from approximately 400 to 700 nm. A hyperspectral camera can extend the wavelength to as high as 2500 nm. This extension will allow engineers to find objects, identify materials, and detect processes on structural surface, which cannot be done with visual inspection. This project aims to develop an open-source catalogue of concrete and steel surfaces and their spectral/spatial features (discoloration, characteristic wavelength, roughness, texture, shape, etc.), extract spatial/spectral features of hyperspectral images, and develop/train a multi-class classification or regression classifier through machine learnings (supervised and/or semi-supervised), and validate the classifier as a decision-making tool for the assessment of concrete crack and degradation processes, in-situ concrete properties, and corrosion process in steel bridges

Hyperspectral Image Analysis for Mechanical and Chemical Properties of Concrete and Steel Surfaces

A typical human eye will respond to wavelengths from approximately 400 to 700 nm. A hyperspectral camera can extend the wavelength to as high as 2500 nm. This extension will allow engineers to find objects, identify materials, and detect processes on structural surface, which cannot be done with visual inspection. This project aims to develop an open-source catalogue of concrete and steel surfaces and their spectral/spatial features (discoloration, characteristic wavelength, roughness, texture, shape, etc.), extract spatial/spectral features of hyperspectral images, and develop/train a multi-class classification or regression classifier through machine learnings (supervised and/or semi-supervised), and validate the classifier as a decision-making tool for the assessment of concrete crack and degradation processes, in-situ concrete properties, and corrosion process in steel bridges

Sensor-Enhanced Analysis and Behavior of Steel Beams in Fire

Traditionally, strain data are difficult, if not impossible, to obtain from steel structures in fire due to their harsh environment and temperature measurements are limited to the locations of thermocouples. This paper presents high-temperature measurements using a Brillouin scattering based (distributed) fiber optic sensor and the application of the measured temperatures and material parameters recommended in building codes into the enhanced thermo-mechanical analysis of simply-supported steel beams subjected to combined thermal and loading effects. The distributed temperature sensor captures detailed, non-uniform temperature distributions that are compared locally with thermocouple measurements by less than 5% at a 95% confidence level. The simulated strains and deflections are validated using measurements from a second distributed fiber optic (strain) sensor and two linear potentiometers, respectively. The results demonstrate that the temperature-dependent material properties specified in the four investigated building codes lead to strain predictions with less than 13% average error at 95% confidence level and that the EN1993-1-2 building code provided the best predictions. However, the implicit consideration of creep in the EN1993-1-2 is adequate up to 600⁰C. More recently, the distributed sensing technology for temperature and strain measurements was applied into small- and large-scale composite floor specimens of a reinforced concrete slab on one or two I-shaped steel beams. The temperature measurements in the reinforced concrete slab were compared with those from limited thermocouples. This paper completes with an experimental investigation of the potential change in the neutral axis of the concrete-steel composite section at elevated temperature

Fiber Optic Sensor Based Corrosion Assessment in Reinforced Concrete Members

In this 50-minute lecture, the fundamental concepts of fiber optic sensors for both distributed and point corrosion measurements are reviewed. For the distributed monitoring of a line bridge component such as steel reinforced girders, Brillouin scattering and fiber Bragg gratings (FBG) can be coupled to measure both temperature and radial strain as an indirect indicator of corrosion process. For the point monitoring of steel structures, long period fiber gratings (LPFG) are specially designed for a direct measurement of mass loss or the loss in cross sectional area of the component. In particular, a Fe-C coated LPFG sensor is introduced for corrosion induced mass loss measurement when Fe-C materials are comparative to the parent steel component to be monitored. The sensing system operates on the principle of LPFG that is responsive to not only thermal and mechanical deformation, but also the change in refractive index of any medium surrounding the optical fiber. Fabrication process of the LPFG is demonstrated through the CO2 laser aided fiber grating system. To enable mass loss measurement, a low pressure chemical vapor deposition (LPCVD) system is introduced to synthesize a graphene/silver nanowire composite film as flexible transparent electrode for the electroplating of a thin Fe-C layer on the curve surface of a LPFG sensor. An integrated sensing package is illustrated for corrosion monitoring and simultaneous strain and temperature measurement. Two bare LPFGs, three Fe-C coated LPFG sensors are multiplexed and deployed inside three miniature, coaxial steel tubes to measure critical mass losses through the penetration of tube walls and their corresponding corrosion rates in the life cycle of an instrumented steel component. The integrated package can be utilized for in-situ deterioration detection in reinforced concrete and steel structures. Assisted by a permanent magnet in pipeline monitoring, both FBG and LPFG sensors are combined with an extrinsic Fabry-Perot interferometer (EFPI) to measure both internal and external thickness reductions without impacting the operation of the pipeline